An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

被引：9

作者：

Dutta, Devajyoti ^{[1
]}

Sharma, Sanjay ^{[1
]}

Sen, G. K. ^{[2
]}

Kannan, B. A. M. ^{[3
]}

Venketswarlu, S. ^{[4
]}

Gairola, R. M. ^{[5
]}

Das, J. ^{[6
]}

Viswanathan, G. ^{[7
]}

机构：

[1] Kohimna Sci Coll, Dept Phys, Jotsoma 797002, Nagaland, India

[2] Jadavpur Univ, Sch Oceanog Studies, Kolkata 700032, India

[3] Indian Meteorol Dept, New Delhi 110003, India

[4] Indian Meteorol Dept, Cyclone Warning Ctr, Visakhapattanam 530023, India

[5] Ctr Space Applicat, Meteorol & Oceanog Div, Ahmadabad 700108, India

[6] Ex Indian Stat Inst, Kolkata 700108, India

[7] Ex ISRO, Radar Dev Cell, Bangalore 560058, Karnataka, India

来源：

ADVANCES IN SPACE RESEARCH | 2011年 / 47卷 / 11期

关键词：

Spectral moments; Rain intensity; Artificial Neural Network; Doppler Weather Radar; Rain gauges; SIZE DISTRIBUTIONS; SQUALL-LINE; REFLECTIVITY; WSR-88D; VARIABILITY; PARAMETERS; ERROR;

D O I：

10.1016/j.asr.2011.02.002

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66 degrees N & 80.23 degrees E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34-18% and 17-3% respectively, compared to Z-R approach. (C) 2011 COSPAR. Published by Elsevier Ltd. All rights reserved.

引用

页码：1949 / 1957

页数：9

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